The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.
翻译:由数据驱动的保健战略制定决定的必要性正在迅速增加。一个可靠的框架有助于确定影响保健提供机构或医院(从这里称为“设施”)市场份额的因素。这一试点研究旨在开发一个由数据驱动的机械学习-回归框架,帮助战略专家制定关键决定,以改进设施市场份额,进而对改善保健服务质量产生影响。美国(美国)保健业务是为研究选择的;考虑涵盖华盛顿州60个关键设施和大约3年的历史数据的数据。在目前的分析中,市场共享被称为设施在潜在竞争设施组别中遇到的总遭遇中的比例。本研究提出了一个新的双管齐下的方法,即竞争性识别和回归方法,以便分别用来评估和预测市场份额。美国(美国)保健业务为研究选择了影响市场份额的模型的相对重要性;考虑涵盖华盛顿州60个关键设施和大约3年的历史数据的数据。在当前的分析中,市场共享被称为“直接循环分析”(DAGAGs),该设施在潜在竞争设施中遇到的总遭遇中的比例。本级标准是用于评估和预测市场份额预测的精度评估模型的精度,该分析设施的精度水平,其精度分析中,其精度是从驱动的精度分析设施的精度分析设施的精度的精度,其精度,其精度为精度的精度的精度,其精度的精度是精度的精度的精度的精度的精度的精度的精度,其精度,其精度的精度是精度,其精度的精度的精度的精度的精度是精度分析。确定的精度分析设施。